Abstract
Medical Image Segmentation (MIS) stands as a cornerstone in medical image analysis, playing a pivotal role in precise diagnostics, treatment planning, and monitoring of various medical conditions. This paper presents a comprehensive and systematic survey of MIS methodologies, bridging the gap between traditional image processing techniques and modern deep learning approaches. The survey encompasses thresholding, edge detection, region-based segmentation, clustering algorithms, and model-based techniques while also delving into state-of-the-art deep learning architectures such as Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), and the widely adopted U-Net and its variants. Moreover, integrating attention mechanisms, semi-supervised learning, generative adversarial networks (GANs), and Transformer-based models is thoroughly explored. In addition to covering established methods, this survey highlights emerging trends, including hybrid architectures, cross-modality learning, federated and distributed learning frameworks, and active learning strategies, which aim to address challenges such as limited labeled datasets, computational complexity, and model generalizability across diverse imaging modalities. Furthermore, a specialized case study on lumbar spine segmentation is presented, offering insights into the challenges and advancements in this relatively underexplored anatomical region. Despite significant progress in the field, critical challenges persist, including dataset bias, domain adaptation, interpretability of deep learning models, and integration into real-world clinical workflows.
Abstract (translated)
医学图像分割（MIS）在医学影像分析中占据核心地位，对于精准诊断、治疗规划以及各种医疗状况的监测至关重要。本文对MIS方法进行了全面而系统的综述，旨在弥合传统图像处理技术和现代深度学习方法之间的差距。该文献涵盖了阈值法、边缘检测、基于区域分割、聚类算法和模型驱动技术等传统方法，并深入探讨了当前最先进的深度学习架构，包括卷积神经网络（CNN）、全卷积网络（FCN）以及广受欢迎的U-Net及其变体。此外，还详细探究了注意力机制、半监督学习、生成对抗网络（GANs）及基于Transformer的模型等领域的集成应用。 除了涵盖已确立的方法之外，本综述还强调了一些新兴趋势，例如混合架构、跨模态学习、联邦和分布式学习框架以及主动学习策略。这些方法旨在解决有限标记数据集、计算复杂性以及在不同成像模式中实现模型泛化等问题带来的挑战。此外，本文还提供了一个关于腰椎分割的专门案例研究，提供了对这一相对未被充分探索解剖区域中的难题与进展的独特见解。 尽管该领域取得了显著进步，但仍存在一些关键性的挑战，包括数据集偏差、领域适应性、深度学习模型的可解释性和将其融入实际临床工作流程的问题。
URL
https://arxiv.org/abs/2510.03318
